Method for differentially quantifying naturally processed hla-restricted peptides for cancer, autoimmune and infectious diseases immunotherapy development

ABSTRACT

The invention relates to a method for quantitatively identifying relevant HLA-bound peptide antigens from primary tissue specimens on a large scale without labeling approaches. This method can not only be used for the development of peptide vaccines, but is also highly valuable for a molecularly defined immunomonitoring and the identification of new antigens for any immunotherapeutic strategy in which HLA-restricted antigenic determinants function as targets, such as a variety of subunit vaccines or adoptive T-cell transfer approaches in cancer, or infectious and autoimmune diseases.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a §371 National Stage Application ofPCT/EP2011/056056, filed Apr. 15, 2011, which claims priority to UnitedKingdom Application No. 1006360.0, filed Apr. 16, 2010, and U.S.Provisional Application No. 61/324,941, filed Apr. 16, 2010.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for quantitatively identifyingrelevant HLA-bound peptide antigens from primary tissue specimens on alarge scale. This method can not only be used for the development ofpeptide vaccines, but is also highly valuable for a molecularly definedimmuno-monitoring and the identification of new antigens for anyimmunotherapeutic strategy in which HLA-restricted antigenicdeterminants function as targets, such as a variety of subunit vaccinesor adoptive T-cell transfer approaches in cancer, or infectious andautoimmune diseases.

2. Description of Related Art

Development of cancer immuno-therapeutics and immuno-therapies ofautoimmune and infectious diseases aiming to induce the immune system'sT-cell arm to fight cancer might be substantially improved by a profoundknowledge of human leukocyte antigen (HLA)-bound peptide presentationlevels on primary malignant tissues. This information is relevant forpeptide vaccines in particular as well as for any other type of T-cellvaccine based on molecular entities such as protein, DNA or RNA. Thiskind of quantitative data has not been available on an “omics”-scalebefore, because common quantitation methods have so far mostly relied ondifferential chemical labeling strategies requiring that all samples tobe compared are processed within a single experiment, which severelylimited the possible scale of such investigations.

A method for identifying peptides a above avoiding the “reverseimmunology”-associated problem was disclosed in EP1508047B1. Asdescribed above, this method can not be used for the quantitation ofsaid peptides. Another method employing a labeling strategy wasdisclosed in WO 2005/076009 which allowed for some quantitation, but noton a larger scale. Other labels were disclosed, for example, in WO03/025576 or by Martin et al in Proteomics 2003, 3, 2208-2220.

Another method was disclosed by Fortier et al (The MHC class I peptiderepertoire is molded by the transcriptome, JEM, Vol. 205, No. 3, Mar.17, 2008 595-610). This method has the disadvantages that it requiresthe dissection of MHC-bound peptides from non-MHC-binding peptides dueto acid elution. This is performed using b2m-knockout cell lines: Thus,this method can not be used for primary—patient—tumour materials. In themethod, primary murine thymocytes were compared to the murine EL4 cellline. The starting amounts had been adjusted by measuring MHC Imolecules. This alone is a strong restriction of the method disclosed byFortier et al. Furthermore, a normalization as it would be required forprimary tissues of different sizes and tissue origin was not applied.Rather, balanced starting materials were used making normalizationobsolete. However, normalization is absolutely necessary for primary(patient) materials.

Stimulation of an immune response is dependent upon the presence ofantigens recognized as foreign by the host immune system. The discoveryof the existence of tumor associated and disease antigens has raised thepossibility of using a host's immune system to intervene in tumorgrowth. Various mechanisms of harnessing both the humoral and cellulararms of the immune system are currently being explored for cancerimmunotherapy.

Specific elements of the cellular immune response are capable ofspecifically recognizing and destroying tumor cells. The isolation ofcytotoxic T-cells (CTL) from tumor-infiltrating cell populations or fromperipheral blood suggests that such cells play an important role innatural immune defenses against cancer. CD8-positive T-cells (T-CD8⁺) inparticular, which recognize peptides bound to class I molecules of themajor histocompatibility complex (MHC). These peptides of usually 8 to10 amino acid residues are derived from proteins or defective ribosomalproducts (DRIPS) located in the cytosol, play an important role in thisresponse. Human MHC-molecules are also designated as humanleukocyte-antigens (HLA).

There are two classes of MHC-molecules: MHC class I molecules that canbe found on most cells having a nucleus. MHC molecules are composed of aalpha heavy chain and beta-2-microglobulin (MHC class I receptors) or analpha and a beta chain (MHC class II receptors), respectively. Theirthree-dimensional conformation results in a binding groove, which isused for non-covalent interaction with peptides. MHC class I presentpeptides that result from proteolytic cleavage of predominantlyendogenous proteins, DRIPs and larger peptides. MHC class II moleculescan be found predominantly on professional antigen presenting cells(APCs), and primarily present peptides of exogenous or transmembraneproteins that are taken up by APCs during the course of endocytosis, andare subsequently processed. Complexes of peptide and MHC class Imolecules are recognized by CD8-positive cytotoxic T-lymphocytes bearingthe appropriate TCR (T-cell receptor), whereas complexes of peptide andMHC class II molecules are recognized by CD4-positive-helper-T cellsbearing the appropriate TCR. It is well known that the TCR, the peptideand the MHC are thereby present in a stoichiometric amount of 1:1:1.

For a peptide to trigger (elicit) a cellular immune response, it mustbind to an MHC-molecule. This process is dependent on the allele of theMHC-molecule and specific polymorphisms of the amino acid sequence ofthe peptide. MHC-class-I-binding peptides are usually 8-12 amino acidresidues in length and usually contain two conserved residues(“anchors”) in their sequence that interact with the correspondingbinding groove of the MHC-molecule. In this way each MHC allele has a“binding motif” determining which peptides can bind specifically to thebinding groove.

In the MHC class I dependent immune reaction, peptides not only have tobe able to bind to certain MHC class I molecules being expressed bytumor cells, they also have to be recognized by T cells bearing specificT cell receptors (TCR).

The antigens that are recognized by the tumor specific cytotoxic Tlymphocytes, that is, their epitopes, can be molecules derived from allprotein classes, such as enzymes, receptors, transcription factors, etc.which are expressed and, as compared to unaltered cells of the sameorigin, up-regulated in cells of the respective tumor.

The current classification of tumor associated or disease associatedantigens comprises the following major groups:

Cancer-testis antigens: The first TAAs [tumor-associated antigens;disease-associated antigens are abbreviated DAA] ever identified thatcan be recognized by T cells belong to this class, which was originallycalled cancer-testis (CT) antigens because of the expression of itsmembers in histologically different human tumors and, among normaltissues, only in spermatocytes/spermatogonia of testis and,occasionally, in placenta. Since the cells of testis do not expressclass I and II HLA molecules, these antigens cannot be recognized by Tcells in normal tissues and can therefore be considered asimmunologically tumor-specific. Well-known examples for CT antigens arethe MAGE family members or NY-ESO-1.

Differentiation antigens: These TAAs are shared between tumors and thenormal tissue from which the tumor arose; most are found in melanomasand normal melanocytes. Many of these melanocyte lineage-relatedproteins are involved in the biosynthesis of melanin and are thereforenot tumor specific but nevertheless are widely used for cancerimmunotherapy. Examples include, but are not limited to, tyrosinase andMelan-A/MART-1 for melanoma or PSA for prostate cancer.

Over-expressed TAAs: Genes encoding widely expressed TAAs have beendetected in histologically different types of tumors as well as in manynormal tissues, generally with lower expression levels. It is possiblethat many of the epitopes processed and potentially presented by normaltissues are below the threshold level for T-cell recognition, whiletheir over-expression in tumor cells can trigger an anticancer responseby breaking previously established tolerance. Prominent examples forthis class of TAAs are Her-2/neu, Survivin, Telomerase or WT1.

Tumor specific antigens: These unique TAAs arise from mutations ofnormal genes (such as β-catenin, CDK4, etc.). Some of these molecularchanges are associated with neoplastic transformation and/orprogression. Tumor specific antigens are generally able to induce strongimmune responses without bearing the risk for autoimmune reactionsagainst normal tissues. On the other hand, these TAAs are in most casesonly relevant to the exact tumor on which they were identified and areusually not shared between many individual tumors.

TAAs arising from abnormal post-translational modifications: Such TAAsmay arise from proteins which are neither specific nor over-expressed intumors but nevertheless become tumor associated by posttranslationalprocesses primarily active in tumors. Examples for this class arise fromaltered glycosylation patterns leading to novel epitopes in tumors asfor MUC1 or events like protein splicing during degradation which may ormay not be tumor specific.

Oncoviral proteins: These TAAs are viral proteins that may play acritical role in the oncogenic process and, because they are foreign(not of human origin), they can evoke a T-cell response. Examples ofsuch proteins are the human papilloma type 16 virus proteins, E6 and E7,which are expressed in cervical carcinoma.

For proteins to be recognized by cytotoxic T-lymphocytes astumor-specific or—associated antigens or disease-specific or -associatedantigens, and to be used in a therapy, particular prerequisites must befulfilled. The antigen should be expressed mainly by tumor cells orinfected cells and not at all or only in comparably small amounts bynormal healthy tissues, for example less by the factor 5, 10 or more.

In infectious diseases there are two possibilities, first the infectedcells express an antigen not expressed by healthy cells—directlyassociated to the infection—or the infected cells over-express anantigen expressed only in very small amounts by healthy cells—theover-expression of an antigen normally found in the peptidome of ahealthy cell.

It is furthermore desirable, that the respective antigen is not onlypresent in a type of tumor, infection or strain, but also in highconcentrations (i.e. copy numbers of the respective peptide per cell).Tumor-specific and tumor-associated antigens and disease-specific ordisease-associated antigens are often derived from proteins directlyinvolved in transformation of a normal cell to a tumor/infected cell dueto a function e.g. in cell cycle control or suppression of apoptosis.

In the case of cancer, additional downstream targets of the proteinsdirectly causative for a transformation may be upregulated and thus maybe indirectly tumor-associated. Such indirect tumor-associated antigensmay also be targets of a vaccination approach (Singh-Jasuja H., EmmerichN. P., Rammensee H. G., Cancer Immunol Immunother. 2004 Mar; 453 (3):187-95). In both cases, it is essential that epitopes are present in theamino acid sequence of the antigen, since such a peptide (“immunogenicpeptide”) that is derived from a tumor associated or disease associatedantigen should lead to an in vitro or in vivo T-cell-response.

Basically, any peptide which is able to bind a MHC molecule may functionas a T-cell epitope. A prerequisite for the induction of an in vitro orin vivo T-cell-response is the presence of a T cell with a correspondingTCR and the absence of immunological tolerance for this particularepitope.

Therefore, TAAs and DAAs are a starting point for the development of atumor vaccine. The methods for identifying and characterizing the TAAsand DAAs are based on the use of CTL that can be isolated from patientsor healthy subjects, or they are based on the generation of differentialtranscription profiles or differential peptide expression patternsbetween tumors and normal tissues.

However, the identification of genes over-expressed in tumor tissues orhuman tumor cell lines, or selectively expressed in such tissues or celllines, does not provide precise information as to the use of theantigens being transcribed from these genes in an immune therapy. Thisis because only an individual subpopulation of epitopes of theseantigens are suitable for such an application since a T cell with acorresponding TCR has to be present and immunological tolerance for thisparticular epitope needs to be absent or minimal. It is thereforeimportant to select only those peptides from over-expressed orselectively expressed proteins that are presented in connection with MHCmolecules against which a functional T cell can be found. Such afunctional T cell is defined as a T cell which upon stimulation with aspecific antigen can be clonally expanded and is able to executeeffector functions (“effector T cell”).

T-helper cells play an important role in orchestrating the effectorfunction of CTLs in anti-tumor immunity. T-helper cell epitopes thattrigger a T-helper cell response of the T_(H1) type support effectorfunctions of CD8-positive killer T cells, which include cytotoxicfunctions directed against tumor cells displaying tumor-associatedpeptide/MHC complexes on their cell surfaces. In this waytumor-associated T-helper cell peptide epitopes, alone or in combinationwith other tumor-associated peptides, can serve as active pharmaceuticalingredients of vaccine compositions which stimulate anti-tumor immuneresponses.

BRIEF SUMMARY OF THE INVENTION

In view of the above, it is therefore the object of the presentinvention to provide a method which, at least in part:

-   -   allows a handling of (human) tissue samples of different amounts        and MHC expression levels, i.e. can be readily applied to        primary tissue samples;    -   is not restricted to cells for which, for example,        beta2m-knockout counterparts have to be generated, i.e. is also        applicable to primary (human) tissue samples;    -   can be performed on a “high-throughput” level; and    -   can be performed incrementally, i.e. increasing an existing        dataset of quantitative data with data of new samples over        years.

Other objects and advantages of the present invention will becomereadily apparent for the person of skill when studying the followingdescription as provided.

In the following description, the invention is described using cancer asan example. Nevertheless, the inventive method can also be applied ininfectious diseases, autoimmune diseases, and parasitic infections aslong as the respective immune answer is a MHC class I involving answer.The invention is furthermore not restricted to human diseases and can beused for mammals, as for example bovines, pigs, horses, cats, dogs,rodents, such as rat, mouse, goats, and other domestic animals oranimals in danger of extinction due to a cancerous disease such as, forexample, the Tasmanian devil.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: shows an exemplary mass spectrum from CDC2-001 demonstrating itspresentation on primary tumor sample GC2464. NanoESI-LCMS was performedon a peptide pool eluted from the GC sample 2464. The mass chromatogramfor m/z 597.3501±0.001 Da, z=2 shows a peptide peak at the retentiontime 151.63 min. B) The detected peak in the mass chromatogram at 151.63min revealed a signal of m/z 597.3501 in the MS spectrum. C) Acollisionally induced decay mass spectrum from the selected precursorm/z 597.3501 recorded in the nanoESI-LCMS experiment at the givenretention time confirmed the presence of CDC2-001 in the GC2464 tumorsample. D) The fragmentation pattern of the synthetic CDC2-001 referencepeptide was recorded and compared to the generated natural TUMAPfragmentation pattern shown in C for sequence verification.

FIG. 2: shows expression profiles of mRNA of selected proteins in normaltissues and in 25 gastric cancer samples; CDC2 (Probeset ID: 203213_at);ASPM (Probeset ID: 219918_s_at).

FIG. 3: shows exemplary results of peptide-specific in vitroimmunogenicity of class I TUMAPs. CD8+ T cells were primed usingartificial APCs loaded with relevant (left panel) and irrelevant peptide(right panel), respectively. After three cycles of stimulation, thedetection of peptide-reactive cells was performed by double stainingwith relevant plus irrelevant A*2402-multimers. Shown cells are gated onlive CD8+ lymphocytes and the numbers in the plots represent percentagesof multimer-positive cells.

FIG. 4: shows a presentation profile for an HLA class I peptide ofPTPRZ1 (tyrosine phosphatase, receptor-type, Z polypeptide 1).

FIG. 5: shows a presentation profile for an HLA class I peptide of CDK1(cyclin-dependent kinase 1).

FIG. 6: shows a presentation profile for an HLA class I peptide of ASPM(asp (abnormal spindle) homolog, microcephaly associated (Drosophila)).

FIG. 7: shows the relative b2m protein expression does not changedominantly in different tissue subtypes.

FIG. 8: shows raw and normalized signal areas for spiked syntheticpeptide from yeast alcohol dehydrogenase 1. In total, seven spikedpeptides covering different retention times, charge states and sequencelengths are used for quality control. The raw areas have been normalizedin a two-tier approach accounting for intra- as well as inter-samplevariations, e.g. removing bias due to changes in the setup of the massspectrometer. Differences within the replicates are shown for sample 969from normal lung tissue.

FIG. 9: shows samples from surgically removed normal and tumor tissue aswell as blood from healthy donors were analyzed in a stepwise approach:

1. HLA ligands from the malignant and healthy tissue were identified anddifferentially quantified by label-free liquid chromatography-coupledmass spectrometry (LCMS) in order to determine over-presented TUMAPs(tumor-associated peptides).

2. Genome-wide messenger ribonucleic acid (mRNA) expression analysis bymicroarrays was used to identify genes over-expressed in the malignanttissue compared with a range of normal organs and tissues.

3. Identified HLA ligands were compared to gene expression data.Over-presented TUMAPs encoded by selectively expressed or over-expressedgenes as detected in step 2 were considered suitable TUMAP candidatesfor a multi-peptide vaccine.

4. Literature research was performed in order to identify additionalevidence supporting the relevance of the identified peptides as TUMAPsand exclude the possibility of the protein playing a crucial role inanother pathway.

5. Peripheral T cells from blood of healthy individuals were tested forreactivity against the TUMAP candidates using several immunoassays (invitro T-cell assays).

FIG. 10: shows a partial LCMS map plotted by OpenMS (M. Sturm and O.Kohlbacher. TOPPView: an open-source viewer for mass spectrometry data.J Proteome.Res 8 (7):3760-3763, 2009) with signal for peptide YLLPAIVHIat 122 min.

DETAILED DESCRIPTION OF THE INVENTION Definitions

The term “presentation profile” or “presentation plot” as used hereinshows the average presentation for a peptide in distinct samplesvisualized in a bar chart. The presentation is expressed in percent asabundance relative to the maximum area. The variation is visualized as95% confidence intervals based on the measured replicates. If thepeptide was identified in a sample but no quantification was possible,it is indicated by the label NA (not available/no area).

The “presentation score” is a measure of peptide overpresentation in onegroup of tissues as compared to another group with values between 0and 1. The smaller the presentation score, the more likely theoverpresentation. If a peptide was not found on any normal ornon-diseased samples, no score can be calculated and is therefore set tozero.

To determine a set of significantly overpresented TUMAPs, one has tochoose a significance threshold (e.g. 0.05) and include/get all TUMAPswith presentation score smaller than this threshold. Thus, thepresentation score reflects the probability of a TUMAP appearingoverpresented by chance.

Statistically, the presentation score may be the p-value of a linearmixed model. In this case, the score models the presentation data (TUMAPfeature areas) incorporating the variation between and within samples asrandom effects and the difference between a e.g. tumor entity and normaltissues as fixed effect. In a more simplified approach, the presentationscore may be the p-value of a t-test.

The presentation score is adjusted for multiple testing using FDR (falsediscovery rate).

The term “quality control” as used herein relates to verifying that thedata as used in the present method meets sufficient criteria in order toallow for it to be used in the present method. One example is the totalnumber of identified sequences and the number of sequences with smallvariance in areas based on peptide quantity reproducibility for everysample as analyzed, that is, how many peptide sequences can beidentified (preferably reliably identified) in a sample and whichpercentage of those identified peptides (such as, for example, 25%, 30%,50% or even 75%) can be assigned with a reliable area with smallvariance (e.g. CV of 20% or less). This is a prerequisite for reliablenormalization between different samples. Usually, the higher the numberof peptide sequences that can be reliably identified the “better” thequality of the data. Another example is controlling the quality ofidentifications and area estimates, for example by “classical” visualinspection of the peptide identification results and of the areareproducibilities, or by statistical analysis.

In a first aspect of the present invention, the object of the inventionis solved by a method for the identification and label-freequantification of MHC ligand peptides on primary tissue samples from atleast one mammal, comprising the steps of

-   -   a) providing at least one diseased primary tissue sample and at        least one sample of primary healthy tissue preferably        corresponding to the diseased tissue,    -   b) isolating MHC ligand peptides from said sample(s),    -   c) performing an HPLC-MS analysis on said MHC ligand peptides,    -   d) extracting the precursor ion signal intensity (area) for each        signal, as derived from step c),    -   e) identifying the sequences of said MHC ligand peptides by        fragment spectra clustering and database search, in order to        group areas between different runs and samples, and normalizing        areas within replicate runs to compensate technical        performance/sensitivity differences between replicate runs        resulting in average quantities for each peptide per sample        including error estimates,    -   f) assigning of said sequences to MHC alleles in order to        generate allele-specific sequence subgroups for a comparison        between different samples, and normalizing between different        samples using the allele-specific subgroups to account for        different sample sizes or MHC expression levels: result is        relative quantities comparable between samples,    -   g) performing a data quality control based on peptide        reproducibility for every sample, verifying that the total        number of identified sequences and the number of sequences with        small variance in areas is as high as possible,    -   h) calculating presentation profiles and scores in order to        determine a potential over-presentation of MHC ligand peptides,    -   i) controlling the quality of identifications and area        estimates, for example by visual inspection of the peptide        identification results and of the area reproducibilites, or        statistical analysis,    -   j) comparing the values detected in said at least one diseased        primary tissue sample with the value(s) obtained from said at        least one primary healthy tissues, and    -   k) quantifying said MHC ligand peptides

In a second aspect of the present invention, the object of the inventionis solved by a method for identifying and quantifying MHC ligandpeptides on primary tissue samples, comprising the steps of

-   -   a) providing at least one diseased tissue sample and at least        one sample of healthy tissue preferably corresponding to the        diseased tissue from at least one mammal,    -   b) isolating MHC ligand peptides from said sample(s),    -   c) performing a HPLC-MS analysis on said MHC ligand peptides,    -   d) extracting the precursor ion signal intensity (area) for each        signal, as derived from step c),    -   e) identifying the sequences of said MHC ligand peptides by        fragment spectra clustering and database search, in order to        group areas between different runs and samples, and normalizing        areas within replicate runs to compensate technical        performance/sensitivity differences between replicate runs        resulting in average quantities for each peptide per sample        including error estimates,    -   f) assigning of said sequences to MHC alleles in order to        generate allele-specific sequence subgroups for a comparison        between different samples, and normalizing between different        samples using the allele-specific subgroups to account for        different sample sizes or MHC expression levels: result is        relative quantities comparable between samples,    -   g) performing a data quality control based on peptide        reproducibility, verifying that the total number of identified        sequences and the number of sequences with small variance in        area is at least 25, preferably 50, and more preferably 100,    -   h) calculating presentation profiles and scores in order to        determine a potential over-presentation of MHC ligand peptides,    -   i) controlling the quality of identifications and area estimates        by statistical analysis,    -   j) comparing the values detected in said at least one diseased        primary tissue sample with the value(s) obtained from said at        least one healthy tissues, and    -   k) quantifying said MHC ligand peptides.

Preferred is a method according to the present invention, wherein thetotal number of identified sequences and the number of sequences withsmall variance in areas is not significantly lower (e.g. less 20%, suchas 10% or even 1%) than in the other samples included in thequantitative comparative analysis.

Preferred is a method according to the present invention, wherein saidMHC ligand peptide is selected from a tumor associated peptide (TAA) ordisease associated peptide (DAA).

Further preferred is a method according to the present invention thatfurther comprises a selection of overrepresented and/or tumor-specificMHC ligand peptides.

Even further preferred is a method according to the present invention,wherein said diseased sample is a tumor sample or a sample of infectedtissue. In the context of the present invention, samples that aredirectly derived from subjects, such as patients, are termed “primary”samples, such as primary tissue or tumor samples, in contrast to samplesof cell lines, such as, for example, established tumor cell lines. Thesamples can be fresh or conserved (e.g. frozen or prepared), as long asthey are suitable for the method according to the invention.

As a preferred example, the HLA peptide pools from shock-frozen(primary) tissue samples can be obtained by immune precipitation fromsolid tissues using for example the HLA-A, -B, -C-specific antibodyw6/32 or the HLA-A*02-specific antibody BB7.2 coupled to CNBr-activatedsepharose, followed by acid treatment, and ultrafiltration. Fordifferent HLA-alleles other specific antibodies known in the art can beused as there are for example GAP-A3 for A*03, B1.23.2 for B-alleles.There are corresponding methods to obtain MHC-class I peptides for othermammals that are well known in the art.

The method according to the invention can also be used in the context ofinfectious diseases, such as viral or bacterial infections, for exampledengue fever, Ebola, Marburg virus, tuberculosis (TB), meningitis orsyphilis, preferable the method is used on antibiotic-resistant strainsof infectious organisms, autoimmune diseases, such as arthritis,parasitic infections, such as malaria and other diseases such as MS andMorbus Parkinson, as long as the immune answer is a MHC class I answer.

Common name of organism or Body parts disease Latin name affectedPrevalence Protozoan organisms Source/ Transmission (Reservoir/ Vector)Babesiosis Babesia B. divergens, red blood cells New York, tick bites B.bigemina, Martha's B. equi, B. microfti, Vineyard, B. duncani Nantucket(different species have worldwide distribution) BalantidiasisBalantidium intestinal coli mucosa Blastocystosis Blastocystisintestinal 2-20% of eating food population contaminated with feces froman infected human or animal Coccidia Cryptosporidium intestineswidespread Dientamoebiasis Dientamoeba intestines up to 10% in ingestingfragilis industrialized water or countries food contaminated with fecesAmoebiasis Entamoeba Intestines areas with poor fecal-oral histolyticasanitation, high transmission population density and tropical regionsGiardia Giardia lumen of the widespread ingestion of lamblia smallintestine dormant cysts in fecal contaminated water or food IsosporiasisIsospora epithelial cells worldwide-less fecal oral belli of smallcommon than route intestines Toxoplasma or Cryptosporidium LeishmaniasisLeishmania cutaneous, Visceral Phlebotomus- mucocutaneous,leishmaniasis- bite of or visceral Worldwide; several Cutaneous speciesof leishmaniasis- nocturnal Old World; phlebotomus Mucocutaneoussandflies leishmaniasis- New World Primary amoebic Naegleria brain rarebut deadly Nasal meningoencephalitis fowleri insufflation ofcontaminated warm fresh water, poorly chlorinated swimming pools, hotsprings, soil Malaria Plasmodium red blood cells tropical-250 Anophelesfalciparum million mosquito, (80% of cases/year bites at cases), nightPlasmodium vivax, Plasmodium ovale, Plasmodium malariae RhinosporidiosisRhinosporidium nose, India and Sri nasal seeberi nasopharynx Lankamucosa came into contact with infected material through bathing incommon ponds Toxoplasmosis- Toxoplasma eyes, brain, widespread-upingestion of Parasitic gondii heart, liver to one third of alluncooked/undercooked pneumonia humans pork/lamb/goat with Toxoplasmabradyzoites, ingestion of raw milk with Toxoplasma tachyzoites,ingestion of contaminated water food or soil with oocysts in cat fecesthat is more than one day old Trichomoniasis Trichomonas female 7.4million sexually vaginalis urogenital tract Americans transmitted (malesinfection asymptomatic) Sleeping sickness Trypanosoma blood lymph 50,000to 70,000 tsetse fly, brucei and central people bites at nervous nightsystems Chagas disease Trypanosoma colon, Mexico, CentralTriatoma/Reduviidae- cruzi esophagus, America, South Insect heart,nerves, America-16-18 Vector, muscle and million bites at blood nightHelminths organisms (worms) Transmission/ Vector Ancylostomiasis/Ancylostoma lungs, small common in penetration Hookworm duodenale,intestine, tropical, warm, of skin by Necator blood moist climates L3larva americanus Anisakiasis Anisakis allergic incidental host ingestionof reaction raw fish, squid, cuttlefish, octopus Roundworm- Ascaris sp.Intestines, common in Parasitic Ascaris liver, tropical and pneumonialumbricoidea appendix, subtropical pancreas, regions lungs, Löffler'ssyndrome Roundworm Baylisascaris depending Baylisascaris on species:procyonis, ingestion of Baylisascaris material melis, contaminatedBaylisascaris by stool transfuga, from Baylisascaris raccoons,columnaris, badgers, Baylisascaris bears, otters, devosi, martensBaylisascaris laevis Brugia lymph nodes tropical regions Arthropodsmalayi, of Asia Brugia timori Tapeworm- Cestoda intestine rare Tapeworminfection Clonorchiasis Clonorchis sinensis; Clonorchis viverriniDicrocoelium gall bladder rare ingestion of dendriticum ants DioctophymeDioctophyme kidneys Worldwide Ingestion of renalis infection renale(typically the undercooked right) or raw freshwater fishDiphyllobothriasis- Diphyllobothrium intestines, Europe, Japan,ingestion of tapeworm latum blood Uganda, Peru, raw fresh Chile waterfish Guinea worm- Dracunculus subcutaneous Sudan Dracunculiasismedinensis tissues, muscle Echinococcosis- Echinococcus liver, lungs,Mediterranean as tapeworm granulosus, kidney, spleen countriesintermediate Echinococcus host, multilocularis, ingestion of E. vogeli,material E. oligarthrus contaminated by feces from a carnivore; asdefinite host, ingestion of uncooked meat (offal) from a herbivoreEchinostoma small intestine Far East ingestion of echinatum raw fish,mollusks, snails Pinworm- Enterobius intestines, widespread;Enterobiasis vermicularis, anus temperate Enterobius regions gregoriiLiver fluke- Fasciola liver, gall Fasciola hepatica freshwaterFasciolosis hepatica, blader in Europe, snails Fasciola Africa,Australia, gigantica the Americas and Oceania; Fasciola gigantica onlyin Africa and Asia, 2.4 million people infected by both speciesFasciolopsiasis- Fasciolopsis intestines East Asia-10 ingestion ofintestinal fluke buski million people infested water plants or water(intermediate host:amphibic snails) Gnathostomiasis Gnathostomasubcutaneous rare-Southeast ingestion of spinigerum, tissues (under Asiaraw or Gnathostoma the skin) undercooked hispidum meat (eg, freshwaterfish, chicken, snails, frogs, pigs) or contaminated water HymenolepiasisHymenolepis ingestion of nana, material Hymenolepis contaminateddiminuta by flour beetles, meal worms, cockroaches Loa loa filariasis,Loa loa Connective rain forest of Tabanidae- Calabar swellings filariatissue, lungs, West Africa-12- horse fly, eye 13 million people bites inthe day Mansonelliasis, Mansonella subcutaneous insect Filariasisstreptocerca layer of skin Metagonimiasis- Metagonimus Siberia,ingestion of intestinal fluke yokogawai Manchuria, undercooked Balkanstates, or salted Israel, Spain fish River blindness Onchocerca skin,eye, Africa, Yemen, Simulium/Black volvulus, tissue Central and fly,bite Onchocerciasis South America during the near cool, fast day flowingrivers Chinese Liver Opisthorchis bile duct 1.5 million consuming Flukeviverrini, people in Russia infected Opisthorchis raw, slightlyfelineus, salted or Clonorchis frozen fish sinensis Paragonimiasis,Paragonimus lungs East Asia ingestion of Lung Fluke westermani; raw orParagonimus undercooked africanus; freshwater Paragonimus crabscaliensis; crayfishes Paragonimus or other kellicotti; crustaceansParagonimus skrjabini; Paragonimus uterobilateralis Schistosomiasis-Schistosoma Africa, skin bilharzia, sp. Caribbean, exposure tobilharziosis or eastern South water snail fever (all America, eastcontaminated types) Asia, Middle with East-200 infected million peoplefresh water snails intestinal Schistosoma intestine, liver, Africa, skinschistosomiasis mansoni spleen, lungs, Caribbean, South exposure to skinAmerica, Asia, water Middle East-83 contaminated million people withinfected Biomphalaria fresh water snails urinary Schistosoma kidney,Africa, Middle skin schistosomiasis haematobium bladder, East exposureto ureters, lungs, water skin contaminated with infected Bulinus sp.snails Schistosomiasis Schistosoma intestine, liver, China, East Asia,skin by Schistosoma japonicum spleen, lungs, Philippines exposure tojaponicum skin water contaminated with infected Oncomelania sp. snailsAsian intestinal Schistosoma South East Asia skin schistosomiasismekongi- exposure to water contaminated with infected Neotricula aperta-fresh water snails Sparganosis Spirometra ingestion of erinaceieuromaterial paei contaminated with infected dog or cat feces (humans:dead-end host) Strongyloidiasis- Strongyloides Intestines, skinParasitic stercoralis lungs, skin penetration pneumonia (Larva currens)Beef tapeworm Taenia Intestines worldwide ingestion of saginatadistribution undercooked beef Pork tapeworm Taenia ingestion of soliumundercooked pork Toxocariasis Toxocara liver, brain, worldwide pica,canis, eyes distribution unwashed Toxocara (Toxocara food cati canis-contamined Visceral larva with migrans, Toxocara Ocular larva eggs,migrans) undercooked livers of chicken Trichinosis Trichinella muscle,more common in ingestion of spiralis, periorbital developing undercookedTrichinella region, small countries due to pork britovi, intestineimproved feeding Trichinella practices in nelsoni, developed Trichinellacountries. nativa Swimmer's itch Trichobilharzia skin regenti, exposureto Schistosomatidae contaminated water (snails and vertebrates) WhipwormTrichuris large intestine, common accidental trichiura, anus worldwideingestion of Trichuris eggs in dry vulpis goods such as beans, rice, andvarious grains or soil contaminated with human fecesElephantiasisLymphatic Wuchereria lymphatic Tropical and mosquito,filariasis bancrofti system subtropical bites at night Other organismsparasitic worm Archiacanthocephala Halzoun Linguatula nasopharynx MidEast ingestion of Syndrome serrata raw or undercooked lymph nodes (eg,meat from infected camels and buffalos) Myiasis Oestroidea, dead orliving Calliphoridae, tissue Sarcophagidae Human Botfly DermatobiaSubcutaneous Central and Mosquitoes hominis tissue South America andbiting flies Candiru Trichomycteridae Urethra Amazon River Urinating inBasin waters inhabited by the fish without proper protection

Examples for autoimmune diseases (including diseases not officiallydeclared to be autoimmune diseases) are Chronic obstructive pulmonarydisease, Ankylosing Spondylitis, Crohn's Disease (one of two types ofidiopathic inflammatory bowel disease “IBD”), Dermatomyositis, Diabetesmellitus type 1, Endometriosis, Goodpasture's syndrome, Graves' disease,Guillain-Barre syndrome (GBS), Hashimoto's disease, Hidradenitissuppurativa, Kawasaki disease, IgA nephropathy, Idiopathicthrombocytopenic purpura, Interstitial cystitis, Lupus erythematosus,Mixed Connective Tissue Disease, Morphea, Myasthenia gravis, Narcolepsy,Neuromyotonia, Pemphigus vulgaris, Pernicious anaemia, Psoriasis,Psoriatic Arthritis, Polymyositis, Primary biliary cirrhosis, Relapsingpolychondritis, Rheumatoid arthritis, Schizophrenia, Scleroderma,Sjogren's syndrome, Stiff person syndrome, Temporal arteritis (alsoknown as “giant cell arteritis”), Ulcerative Colitis (one of two typesof idiopathic inflammatory bowel disease “IBD”), Vasculitis, Vitiligoand Wegener's granulomatosis.

The present invention is not restricted to human diseases, but can beused for mammals, for example cows, pigs, horses—preferably racinghorses, cats, dogs, rodents, such as rat, mouse, goat, and otherdomestic animals or mammals in danger of extinction due to a cancerousdisease as for example the Tasmanian devil.

In yet another preferred embodiment of the method according to thepresent invention, the controlling of the quality, at least in part,comprises the use an automate and/or manual or visual inspection.

Yet another preferred embodiment of the method according to the presentinvention further comprises at least one step selected from the group ofin step d) aligning the retention time to assign corresponding signalswithin replicate runs without a knowledge about the sequences, and/orassigning the corresponding signals between different samples byretention time alignment without a knowledge about the sequences, instep h) implementing relative presentation data from more than onepeptide per gene to calculate a gene-centered over-presentation score,and in step i) performing an quality control based on spiked peptides.

Most preferably, the method according to the present invention isperformed in vitro.

In still another preferred embodiment of the method according to thepresent invention, the steps of said method are performed in the orderas indicated in the claim or as above. In still another preferred methodaccording to the present invention said method consists of the steps asindicated above and herein.

In a further preferred aspect of the method according to the presentinvention, said method further comprises the step of synthesizing saidat least one MHC ligand peptide as identified and/or quantified by saidmethod on a synthesizer or manually.

Preferred is a method according to the present invention that furthercomprises the step of testing said MHC ligand peptide as a synthesizedpeptide or purified peptide for its immunogenicity. Respective methodsare known to the person of skill and described both in the respectiveliterature and herein.

In a further preferred aspect of the method according to the presentinvention, said method relates to personalized therapy and diagnosis.For this, said sample(s) as analyzed is/are derived from one individual.Also, a personalized MHC ligand profile, preferably a personalizeddisease-specific MHC ligand profile, based on said MHC ligand peptidesas identified and/or quantified can be generated based on the methodaccording to the present invention as described herein.

In a further preferred aspect of the method according to the presentinvention, said total number of identified sequences and the number ofsequences with small variances in areas is higher than 5, preferablyhigher than 25, more preferably higher than 50, and most preferredhigher than 100.

Surprisingly, in the context of the present invention the inventorsfound that by combining an expression analysis with the quantificationof antigenic tumor peptides that have been isolated and analyzed,specific candidates for an individual vaccine can be readily identified.For the first time, this new approach as provided by the inventorsallows for the identification and selection of relevant over-presentedpeptide vaccine candidates based on a direct relative quantitation ofMHC-, preferably HLA-restricted, peptide levels on cancer or otherinfected tissues in comparison to several different non-canceroustissues or no-infected tissues and organs. This was achieved by thedevelopment of label-free differential quantitation using liquidchromatography-coupled mass spectrometry data processed by a proprietarydata analysis pipeline, combining algorithms for sequenceidentification, spectral clustering, ion counting, and normalization.The approach can be applied to peptides from any HLA class I allele, androutine quantitation has already been performed for two very commonalleles and several different tumor entities as an example. Extensivevalidation experiments confirmed the accuracy of our results. Thenormalization strategy was corroborated by measurements of beta-2microglobulin levels in affinity chromatography elutions by westernblotting.

Commonly used “reverse immunology” approaches, which are based onprediction algorithms to select HLA-binding peptides out of a proteinsequence are hampered by the problem that these approaches neglect theimportant step of antigen processing. Most of the peptides selected thisway are not presented physiologically on cells of primary tissues orcell lines because they bind to HLA but are not processed from theprotein.

In another preferred embodiment of the method according to the presentinventions, said method is label-free, i.e. does exclude the use oflabels, in particular chemical labels, for example for the peptides tobe analyzed.

In yet another preferred embodiment of the method according to thepresent invention, said method does furthermore exclude the use ofknock-out cells, cell lines or animals.

In one further preferred aspect of the method according to theinvention, the isolated MHC/HLA ligands are separated according to theirhydrophobicity by reversed-phase chromatography (e.g. nanoAcquity UPLCsystem, Waters) followed by detection in an LTQ-Orbitrap hybrid massspectrometer (ThermoElectron). Each sample is analyzed label-free byacquisition of five replicate LCMS runs. The LCMS data is processed byanalyzing the LCMS survey as well as the Tandem-MS (MS/MS) data. Thedata analysis is optimized to handle incremental and replicated sampleacquisition.

The tandem-MS spectra as generated were extracted using msn_extract(ThermoScientific) and searched with Sequest against a protein databasesuch as the IPI database. The protein database hits were subsequentlyvalidated by automated quality filtering using thresholds optimized forHLA peptidomics data.

For increased identification sensitivity, an in-house developed spectralclustering algorithm was used to assign spectra to known peptide MS/MSwhich are being collected in the IFL (Immatics Fragment spectraLibrary). The MS/MS scans of a new LCMS run were added incrementally tothe IFL scan by scan. The clustering uses a growing k-means clusteringalgorithm adapted for spectral data.

Due to the sequence identification and fragment spectra clustering, itis then possible to group intensity values (areas) for the same sequenceor cluster from different runs and samples.

Comparability of peptide groups restricted to the same HLA allelebetween different samples is possible based on a common allele-specificantibody used for purification if available or alternatively based onassignment of sequences to common HLA-alleles by means of anchor aminoacid patterns.

Each new data set is integrated in a database (for example MySQL) andcross-referenced to available proteomic, genomic and literature data.

LCMS survey data is analyzed independently using ion counting and makinguse of the high-mass accuracy. To extract LCMS signals as well as theintegrated signal areas a feature finding algorithms for exampleimplemented by the program SuperHirn (L. N. Mueller, 0. Rinner, A.Schmidt, S. Letarte, B. Bodenmiller, M. Y. Brusniak, O. Vitek, R.Aebersold, and M. Muller. SuperHirn—a novel tool for high resolutionLC-MS-based peptide/protein profiling. Proteomics. 7 (19):3470-3480,2007.) can be used.

Combining both data sets yields quantitative data for each identifiedpeptide, which can subsequently be normalized in a two-tier approachbased on central tendency normalization to account for variation betweentechnical replicates (LC-MS runs) and between samples of differenttissue origin such as tumor and healthy tissues (see above). The latterdifferences can be, for example, due to or derived from different MHCexpression levels or different amounts of starting materials.

For utmost reliability, in a preferred embodiment, only peptides, whichhave a coefficient of variation (CV) of smaller than 25%, preferably20%) between their replicate areas, are considered in the firstnormalization step.

To extract over-presented peptides, a presentation profile is calculatedshowing the median sample presentation as well as replicate variation.The profile juxtaposes samples of the tumor entity of interest to abaseline of normal tissue samples. Each of these profiles can then beconsolidated into an over-presentation score by calculating the p-valueof a Linear Mixed-Effects Model (J. Pinheiro, D. Bates, S. DebRoy,Sarkar D., R Core team. nlme: Linear and Nonlinear Mixed Effects Models.2008) adjusting for multiple testing by False Discovery Rate (Y.Benjamini and Y. Hochberg. Controlling the False Discovery Rate: APractical and Powerful Approach to Multiple Testing. Journal of theRoyal Statistical Society. Series B (Methodological), Vol.57(No.1):289-300, 1995).

Sequence assignment and areas for peptides selected as potential vaccinecandidates are confirmed by manual inspection.

A further preferred optional step of the present invention is thealignment of retention times between replicate LC-MS runs to avoid therequirement for sequence assignment to signals for the firstnormalization step.

A further preferred optional step of the present invention is thealignment of retention times between different samples to avoid therequirement for sequence assignment to signals for the secondnormalization step.

A further preferred optional step of the present invention is thecalculation of a gene-centered over-presentation score by combination ofsingle peptide over-presentation scores if more than one peptide isidentified from the same gene.

A further preferred optional step of the present invention is anautomatic quality control of replicate normalization based on moleculesspiked into the samples in defined amounts.

By isolating antigenic peptides and matching them with gene expressionand presentation on the cells leading to profiles of tumorous tissue, itcan be avoided that a vast number of possible immunoreactive peptides isobtained. Rather, specific peptides are identified according to thepresent invention, which are actually presented by MHC-molecules andwhich are thus suitable as immunoreactive peptides.

With the method according to the invention it is furthermore possible toidentify patient-specific peptides, i.e. it is possible to preciselymatch peptides, which are to be used as vaccine, to the patient in orderto induce a specific immune response.

For example, industrial laboratories—after having received patientsamples—can systematically and efficiently perform the present method,and can—after having identified suitable immunoreactive peptides—provideclinics in charge with the peptide sequences; the clinics can thensynthesize and administer the peptides. Nevertheless, it is alsopossible that a laboratory is carrying out identification as well asproduction of the peptides suitable for the respective patient.

Therefore, the new method of the present invention is applicable withinthe scope of a mere service as well as in combination with the supply ofthe identified immunoreactive peptide.

Further aspects of the invention are immunoreactive peptides, which areidentified and/or prepared by the method according to the invention.After identification these peptides can be selectively and specificallyprepared for the treatment of the patient.

Another aspects of the invention then relates to a pharmaceuticalcomposition comprising one or more peptides that have been identifiedand/or prepared by the method according to the invention.

The composition may be applied, for example, parenterally, for examplesubcutaneously, intradermally or intramuscularly, or may be administeredorally, depending on the formulation and the target disease. In doingso, the peptides are dissolved or suspended in a pharmaceuticallyacceptable carrier, preferably an aqueous carrier; the composition canfurther comprise additives, for example buffers, binders, etc. Thepeptides can also be administered together with immunostimulatingsubstances, for example cytokines.

According to one aspect of the invention, the peptides may be used forthe treatment of tumorous diseases and for preparing a drug fortreatment of tumor diseases.

Tumorous diseases to be treated comprise solid tumors, such as renal,breast, pancreas, gastric, testis and/or skin cancer or blood cancerssuch as AML. This list of tumor diseases is only exemplary, and is notintended to limit the area of application.

The peptides can further be used for assessment of the therapy-course ofa tumor disease.

The peptides can also be used for monitoring a therapy in otherimmunizations or therapies. Therefore, the peptide may not only be usedtherapeutically but also diagnostically.

A further aspect of the invention then relates to the use of thepeptides for generating an antibody. Polyclonal antibodies can beobtained, in a general manner, by immunization of animals by means ofinjection of the peptides and subsequent purification of theimmunoglobulin. Monoclonal antibodies can be generated according tostandardized protocols known in the art.

Further aspects of the invention are nucleic acid molecules encoding forthe peptide isolated with the method according to the invention. Thenucleic acid molecules can be DNA- or RNA-molecules and can be used forimmune therapy of cancer as well. According to one aspect of theinvention the nucleic acid molecules can be provided in a vector, suchas, for example, an expression vector.

A further aspect of the invention relates to a cell that is geneticallymodified by means of the nucleic acid molecule (e.g. in an expressionvector), such, that the cell is producing a peptide identified accordingto the invention.

Another aspect of the invention relates to a method for preparing animmunoreactive peptide with which a peptide is identified according tothe disclosed method and the identified peptide is synthesizedchemically, in vitro or in vivo. Peptides can be prepared by chemicallinkage of amino acids by the standard methods known in the art.

Peptides can be prepared in vitro, for example, in cell-free systems,and in vivo using cells. The peptides can be formulated as disclosed,for example, in EP2111867 by Lewandrowski et al.

Yet a further aspect relates to the method according to the inventioncomprising a further step in which the reactivity of peripheralleukocytes, preferably of T-leukocytes, against the isolated antigenicpeptides, is tested.

A further aspect relates to the method according to the invention,wherein the reactivity of peripheral leukocytes against the isolatedantigenic peptides is tested by means of measuring γ-Interferon-mRNAand/or cytokin-mRNA synthesised by the leukocytes.

By detecting γ-Interferon- or cytokin-mRNA it is possible to preciselyprove the specific reactivity of leukocytes, preferably of T-lymphocytesagainst antigenic peptides. Both substances are secreted by activatedT-lymphocytes after their activation by corresponding antigenicpeptides.

Yet another aspect relates to the method according to the invention,wherein a further step is performed, in which the presence of theT-lymphocytes is detected. Using this method, it is possible tospecifically detect to what extent T-lymphocytes directed againstisolated and identified peptides are pre-existing in patients. Byperforming this step it is possible to apply, as a vaccine, only thosepeptides against which T-lymphocytes are already pre-existing in thepatient. The peptides can then be used to activate these specificT-lymphocytes.

A further aspect relates to the method according to the invention,wherein the detection of specific pre-existing T-lymphocytes isperformed by labelling the leukocytes with reconstituted complexes ofantigen-presenting molecules and antigenic peptide.

More than 11,000 different peptides have been quantified on 70 cancerand 40 non-cancerous samples by the inventors so far. Significancescores for over-presentation within individual cancer entities as wellas average presentation levels including confidence intervals for everysingle peptide and sample were established. Peptides exclusivelypresented on tumor tissue and peptides over-presented in tumor versusnon-cancerous tissues and organs have been identified.

The term “peptide” is used herein to designate a series of amino acidresidues, connected one to the other typically by peptide bonds betweenthe alpha-amino and carbonyl groups of the adjacent amino acids. Thepeptides are preferably 9 amino acids in length, but can be as short as8 amino acids in length, and as long as 10, 11, 12, 13 or 14 amino acidsin length.

The term “oligopeptide” is used herein to designate a series of aminoacid residues, connected one to the other typically by peptide bondsbetween the alpha-amino and carbonyl groups of the adjacent amino acids.The length of the oligopeptide is not critical to the invention, as longas the correct epitope or epitopes are maintained therein. Theoligopeptides are typically less than about 30 amino acid residues inlength, and greater than about 14 amino acids in length.

The term “polypeptide” designates a series of amino acid residues,connected one to the other typically by peptide bonds between thealpha-amino and carbonyl groups of the adjacent amino acids. The lengthof the polypeptide is not critical to the invention as long as thecorrect epitopes are maintained. In contrast to the terms peptide oroligopeptide, the term polypeptide is meant to refer to moleculescontaining more than about 30 amino acid residues.

A peptide, oligopeptide, protein or polynucleotide coding for such amolecule is “immunogenic” (and thus an “immunogen” within the presentinvention), if it is capable of inducing an immune response. In the caseof the present invention, immunogenicity is more specifically defined asthe ability to induce a T-cell response. Thus, an “immunogen” would be amolecule that is capable of inducing an immune response, and in the caseof the present invention, a molecule capable of inducing a T-cellresponse.

A T cell “epitope” requires a short peptide that is bound to a class IMHC receptor, forming a ternary complex (MHC class I alpha chain,beta-2-microglobulin, and peptide) that can be recognized by a T cellbearing a matching T-cell receptor binding to the MHC/peptide complexwith appropriate affinity. Peptides binding to MHC class I molecules aretypically 8-14 amino acids in length, and most typically 9 amino acidsin length.

In humans, there are three different genetic loci that encode MHC classI molecules (the MHC-molecules of the human are also designated humanleukocyte antigens (HLA)): HLA-A, HLA-B, and HLA-C. HLA-A*01, HLA-A*02,and HLA-A*024 are examples of different MHC class I alleles that can beexpressed from these loci.

Immunotherapeutic Approaches for Treatment

Stimulation of an immune response is dependent upon the presence ofantigens recognized as foreign by the host immune system. The discoveryof the existence of tumor associated antigens has now raised thepossibility of using a host's immune system to intervene in tumorgrowth. Various mechanisms of harnessing both the humoral and cellulararms of the immune system are currently explored for cancerimmunotherapy.

Specific elements of the cellular immune response are capable ofspecifically recognizing and destroying tumor cells. The isolation ofcytotoxic T-cells (CTL) from tumor-infiltrating cell populations or fromperipheral blood suggests that such cells play an important role innatural immune defenses against cancer. CD8-positive T-cells inparticular, which recognize class I molecules of the majorhistocompatibility complex (MHC)-bearing peptides of usually 8 to 12residues derived from proteins or defect ribosomal products (DRIPS)located in the cytosols, play an important role in this response. TheMHC-molecules of the human are also designated as humanleukocyte-antigens (HLA).

MHC class I molecules can be found on most cells having a nucleus whichpresent peptides that result from proteolytic cleavage of mainlyendogenous, cytosolic or nuclear proteins, DRIPS, and larger peptides.However, peptides derived from endosomal compartments or exogenoussources are also frequently found on MHC class I molecules. Thisnon-classical way of class I presentation is referred to ascross-presentation in literature.

For proteins to be recognized by cytotoxic T-lymphocytes astumor-specific or—associated antigens, and to be used in a therapy,particular prerequisites must be fulfilled. The antigen should beexpressed mainly by tumor cells and not by normal healthy tissues or incomparably small amounts. It is furthermore desirable, that therespective antigen is not only present in a type of tumor, but also inhigh concentrations (i.e. copy numbers of the respective peptide percell). Tumor-specific and tumor-associated antigens are often derivedfrom proteins directly involved in transformation of a normal cell to atumor cell due to a function e.g. in cell cycle control or apoptosis.Additionally, also downstream targets of the proteins directly causativefor a transformation may be upregulated and thus be indirectlytumor-associated. Such indirectly tumor-associated antigens may also betargets of a vaccination approach. Essential is in both cases thepresence of epitopes in the amino acid sequence of the antigen, sincesuch peptide (“immunogenic peptide”) that is derived from a tumorassociated or disease associated antigen should lead to an in vitro orin vivo T-cell-response.

Basically, any peptide able to bind a MHC molecule may function as aT-cell epitope. A prerequisite for the induction of an in vitro or invivo T-cell-response is the presence of a T cell with a correspondingTCR and the absence of immunological tolerance for this particularepitope. Therefore, TAAs are a starting point for the development of atumor vaccine. The methods for identifying and characterizing the TAAsare based on the use of CTL that can be isolated from patients orhealthy subjects, or they are based on the generation of differentialtranscription profiles or differential peptide expression patternsbetween tumors and normal tissues (Lemmel et al. 450-54;Weinschenk etal. 5818-27). However, the identification of genes over-expressed intumor tissues or human tumor cell lines, or selectively expressed insuch tissues or cell lines, does not provide precise information as tothe use of the antigens being transcribed from these genes in an immunetherapy. This is because only an individual subpopulation of epitopes ofthese antigens are suitable for such an application since a T cell witha corresponding TCR has to be present and immunological tolerance forthis particular epitope needs to be absent or minimal. It is thereforeimportant to select only those peptides from over-expressed orselectively expressed proteins that are presented in connection with MHCmolecules against which a functional T cell can be found. Such afunctional T cell is defined as a T cell that upon stimulation with aspecific antigen can be clonally expanded and is able to executeeffector functions (“effector T cell”).

T-helper cells play an important role in orchestrating the effectorfunction of CTLs in anti-tumor immunity. T-helper cell epitopes thattrigger a T-helper cell response of the TH1 type support effectorfunctions of CD8-positive killer T cells, which include cytotoxicfunctions directed against tumor cells displaying tumor-associatedpeptide/MHC complexes on their cell surfaces. In this waytumor-associated T-helper cell peptide epitopes, alone or in combinationwith other tumor-associated peptides, can serve as active pharmaceuticalingredients of vaccine compositions that stimulate anti-tumor immuneresponses.

Since both types of response, CD8 and CD4 dependent, contribute jointlyand synergistically to the anti-tumor effect, the identification andcharacterization of tumor-associated antigens recognized by either CD8+CTLs (MHC class I molecule) or by CD4-positive CTLs (MHC class IImolecule) is important in the development of tumor vaccines. It istherefore an object of the present invention, to provide compositions ofpeptides that contain peptides binding to MHC complexes of either class.

Considering the severe side-effects and expenses associated withtreating cancer better prognosis and diagnostic methods are desperatelyneeded. Therefore, there is a need to identify other factorsrepresenting biomarkers for cancer in general and gastric cancer inparticular. Furthermore, there is a need to identify factors that can beused in the treatment of cancer in general and gastric cancer inparticular.

Furthermore there is no established therapeutic design for gastriccancer patients with biochemical relapse after radical prostatectomy,usually caused by residual tumor left in situ in the presence of locallyadvanced tumor growth. New therapeutic approaches that confer lowermorbidity with comparable therapeutic efficacy relative to the currentlyavailable therapeutic approaches would be desirable.

The present invention provides peptides that are useful in treatinggastric cancer and other tumors that over-present the peptides of theinvention. These peptides were shown by mass spectrometry to benaturally presented by HLA molecules on primary human gastric cancersamples (see Example 1 and FIG. 1).

The source gene from which the peptides are derived were shown to behighly over-expressed in gastric cancer, renal cell carcinoma, coloncancer, non-small cell lung carcinoma, adenocarcinoma, prostate cancer,benign neoplasm and malignant melanoma compared with normal tissues (seeExample 2 and FIG. 2) demonstrating a high degree of tumor associationof the peptide, i.e. these peptides are strongly presented on tumortissue but not on normal tissues. MHC/HLA-bound peptides can berecognized by the immune system, specifically T lymphocytes/T cells. Tcells can destroy the cells presenting the recognized MHC- orHLA-peptide complex, e.g. gastric cancer cells presenting the derivedpeptides.

The invention shall now be described further in the following examples,nevertheless, without being limited thereto. In the accompanying Figuresand the Sequence Listing,

FIG. 1: shows an exemplary mass spectrum from CDC2-001 demonstrating itspresentation on primary tumor sample GC2464. NanoESI-LCMS was performedon a peptide pool eluted from the GC sample 2464. The mass chromatogramfor m/z 597.3501±0.001 Da, z=2 shows a peptide peak at the retentiontime 151.63 min. B) The detected peak in the mass chromatogram at 151.63min revealed a signal of m/z 597.3501 in the MS spectrum. C) Acollisionally induced decay mass spectrum from the selected precursorm/z 597.3501 recorded in the nanoESI-LCMS experiment at the givenretention time confirmed the presence of CDC2-001 in the GC2464 tumorsample. D) The fragmentation pattern of the synthetic CDC2-001 referencepeptide was recorded and compared to the generated natural TUMAPfragmentation pattern shown in C for sequence verification.

FIG. 2: shows expression profiles of mRNA of selected proteins in normaltissues and in 25 gastric cancer samples; CDC2 (Probeset ID: 203213_at);ASPM (Probeset ID: 219918_s_at).

FIG. 3: shows exemplary results of peptide-specific in vitroimmunogenicity of class I TUMAPs. CD8+ T cells were primed usingartificial APCs loaded with relevant (left panel) and irrelevant peptide(right panel), respectively. After three cycles of stimulation, thedetection of peptide-reactive cells was performed by double stainingwith relevant plus irrelevant A*2402-multimers. Shown cells are gated onlive CD8+ lymphocytes and the numbers in the plots represent percentagesof multimer-positive cells.

FIG. 4: shows a presentation profile for an HLA class I peptide ofPTPRZ1 (tyrosine phosphatase, receptor-type, Z polypeptide 1).

FIG. 5: shows a presentation profile for an HLA class I peptide of CDK1(cyclin-dependent kinase 1).

FIG. 6: shows a presentation profile for an HLA class I peptide of ASPM(asp (abnormal spindle) homolog, microcephaly associated (Drosophila)).

FIG. 7: shows the relative b2m protein expression does not changedominantly in different tissue subtypes.

FIG. 8: shows raw and normalized signal areas for spiked syntheticpeptide from yeast alcohol dehydrogenase 1. In total, seven spikedpeptides covering different retention times, charge states and sequencelengths are used for quality control. The raw areas have been normalizedin a two-tier approach accounting for intra- as well as inter-samplevariations, e.g. removing bias due to changes in the setup of the massspectrometer. Differences within the replicates are shown for sample 969from normal lung tissue.

FIG. 9: shows samples from surgically removed normal and tumor tissue aswell as blood from healthy donors were analyzed in a stepwise approach:

1. HLA ligands from the malignant and healthy tissue were identified anddifferentially quantified by label-free liquid chromatography-coupledmass spectrometry (LCMS) in order to determine over-presented TUMAPs(tumor-associated peptides).

2. Genome-wide messenger ribonucleic acid (mRNA) expression analysis bymicroarrays was used to identify genes over-expressed in the malignanttissue compared with a range of normal organs and tissues.

3. Identified HLA ligands were compared to gene expression data.Over-presented TUMAPs encoded by selectively expressed or over-expressedgenes as detected in step 2 were considered suitable TUMAP candidatesfor a multi-peptide vaccine.

4. Literature research was performed in order to identify additionalevidence supporting the relevance of the identified peptides as TUMAPsand exclude the possibility of the protein playing a crucial role inanother pathway.

5. Peripheral T cells from blood of healthy individuals were tested forreactivity against the TUMAP candidates using several immunoassays (invitro T-cell assays).

FIG. 10: shows a partial LCMS map plotted by OpenMS (M. Sturm and O.Kohlbacher. TOPPView: an open-source viewer for mass spectrometry data.J Proteome.Res 8 (7):3760-3763, 2009) with signal for peptide YLLPAIVHIat 122 min.

SEQ ID No. 1 to 27 show the peptides of table 3 and the examples.

EXAMPLES

The following examples describe the inventive method in the context ofTAAs/cancer. The invention is not restricted to the examples, as theyare only one preferred embodiment of the invention. For the purposes ofthe present invention, all references as cited herein are incorporatedby reference in their entireties.

Methods:

Tissue Samples

Patients' tumor and normal tissues were provided by several differenthospitals depending on the tumor entity analyzed. Written informedconsents of all patients had been given before surgery. Tissues wereshock-frozen in liquid nitrogen immediately after surgery and storeduntil isolation of TUMAPs at −80° C.

Isolation of HLA Peptides from Tissue Samples

HLA peptide pools from shock-frozen tissue samples were obtained byimmune precipitation from solid tissues according to a slightly modifiedprotocol using the HLA-A, -B, -C-specific antibody W6/32, theHLA-A*02-specific antibody BB7.2, CNBr-activated sepharose, acidtreatment, and ultrafiltration. For different HLA-alleles other specificantibodies known in the art can be used as there are for example GAP-A3for A*03, B1.23.2 for B-alleles.

Mass Spectrometry

The HLA peptide pools as obtained were separated according to theirhydrophobicity by reversed-phase chromatography (nanoAcquity UPLCsystem, Waters) and the eluting peptides were analyzed in anLTQ-Orbitrap hybrid mass spectrometer (ThermoElectron) equipped with anESI source. Peptide pools were loaded directly onto the analyticalfused-silica micro-capillary column (75 μm i.d.×250 mm) packed with 1.7μm C18 reversed-phase material (Waters) applying a flow rate of 400 nLper minute. Subsequently, the peptides were separated using a two-step180 minute-binary gradient from 10% to 33% B at a flow rate of 300 nLper minute. The gradient was composed of Solvent A (0.1% formic acid inwater) and solvent B (0.1% formic acid in acetonitrile). A gold coatedglass capillary (PicoTip, New Objective) was used for introduction intothe nanoESI source. The LTQ-Orbitrap mass spectrometer was operated inthe data-dependent mode using a TOP5 and a TOP3 strategy. In brief, ascan cycle was initiated with a full scan of high mass accuracy in theorbitrap (R=30 000 for TOP3, R=60000 for TOP5), which was followed byMS/MS scans either in the Orbitrap (R=7500) on the 5 most abundantprecursor ions with dynamic exclusion of previously selected ions (TOP5)or in the LTQ on the 3 most abundant precursor ions with dynamicexclusion of previously selected ions (TOP3).

Data Analysis

For each sample, five label-free replicate LCMS runs have been acquired,two in TOP5 mode and three runs in TOP3 mode. The LCMS data wasprocessed by an in-house pipeline analyzing the LCMS survey as well asthe Tandem-MS (MS/MS) data. The proprietary data analysis is optimizedand adapted for our incremental, replicate acquisition setting and thepeptidomics data which is not handled satisfactory by standardproteomics software. Each new data set is integrated in the ImmaticsDiscovery database (MySQL). All data is cross-referenced to availableproteomic, genomic and literature data.

Sequence Identification

Tandem-MS spectra were extracted using msn_extract (ThermoScientific)and searched with Sequest against the IPI database. The protein databasehits are subsequently validated by automated quality filtering usingthresholds optimized for HLA peptidomics data. Interesting peptidevaccine candidates are further confirmed by manual inspection.

The identified peptide sequence was assured by comparison of thegenerated natural peptide fragmentation pattern with the fragmentationpattern of a synthetic sequence-identical reference peptide. FIG. 1shows an exemplary spectrum obtained from tumor tissue for the MHC classI associated peptide CDC2-001 and its elution profile on the UPLCsystem.

For increased identification sensitivity, an in-house developed spectralclustering algorithm was used to assign spectra to known peptide MS/MSwhich are being collected in the IFL (Immatics Fragment spectraLibrary). The MS/MS scans of a new LCMS run are added incrementally tothe IFL scan by scan. The clustering uses a growing k-means clusteringalgorithm adapted for spectral data. Pairwise similarity between scansis measured by MuQest (ThermoScientific). Each cluster is represented bya consensus spectrum. These consensus spectra can be used to speed updownstream analyses by removing the spectral redundancy. Since consensusspectra result from averaging experimental MS/MS spectra, precursormasses are more accurate and the spectra show less noise.

Relative TUMAP Quantification

LCMS survey data was analyzed independently of the Tandem-MS making useof the high-mass accuracy. To extract LCMS signals as well as the signalareas (ion counting) the program SuperHirn (ETH Zürich) was used. Thuseach identified peptide can be associated with quantitative dataallowing relative quantification between samples and tissues.

To account for variation between technical and biological replicates, atwo-tier normalization scheme was used based on central tendencynormalization. The normalization assumes that most measured signalsresult from house-keeping peptides and the small fraction ofover-presented peptides does not influence the central tendency of thedata significantly. In the first normalization step the replicates ofthe same sample are normalized by calculating the mean presentation foreach peptide in the respective replicate set. This mean is used tocompute normalization factors for each peptide and LC-MS run. Averagingover all peptides results in run-wise normalization factors which areapplied to all peptides of the particular LCMS run. This approachensures that systematic intra-sample variation is removed, e.g. due todifferent injection volumes between the replicate runs.

Only peptides, which have a coefficient of variation smaller than 25%between their replicate areas, are considered in the next normalizationstep. Again the mean presentation of each peptide is calculated, thistime for all samples of a defined preparation antibody (e.g. BB7.2). Themean is used to compute normalization factors for each peptide andsample. Averaging over all peptides results in sample-wise normalizationfactors, which are applied to all peptides of the particular sample.Systematic bias due to different tissue weights or MHC expression levelsis therefore removed.

To extract over-presented peptides, a presentation profile wascalculated for each peptide showing the mean sample presentation as wellas replicate variations. The profile juxtaposes samples of the tumorentity of interest to a baseline of normal tissue samples. Each of theseprofiles was consolidated into an over-presentation score by calculatingthe p-value of a Linear Mixed-Effects Models (GNU R) adjusting formultiple testing by False Discovery Rate. Ranking all peptides by theirp-values yields the most promising vaccine candidate from a presentationpoint of view.

Quantitation of Beta2m-Microglobulin

Beta-2-microglobulin (b2m) was quantified by western blotting, usingbeta-actin as reference protein to normalize for protein amounts loadedon the gels.

Results

Development of TUMAP Quantitation using Cell Lines

To establish the quantitation pipeline, different methods have beenapplied. First a dilution experiment was conducted using the JY cellline in 2 and 20% dilution to simulate sample variation. For eachdiluted sample three replicates have been acquired. After normalizationof inter-run and inter-sample variation the normalization factor betweenthe two samples was 9.95.

In a second experiment another JY sample was spiked with a trypticallydigested MassPREP™ (Waters) protein mix (Phosphorylase b, BSA, BovineHemoglobin, Enolase, ADH) in 100, 10 and 1 fmol dilutions. Again, threereplicates for each concentration were acquired. After data analysis,the mean ratio between the known spiked peptides was 90, 8 and 11 ifcomparing the 1 versus the 100 fmol sample, 100 vs. 10 fmol and 10 vs. 1fmol, respectively.

In addition, we compared different acquisition methods to maximize thenumber and accuracy of peptide identifications as well as thequantitative precision of our setting. For this purpose, we examinedabout 50 replicates of a JY sample in different MS/MS modes.

Primary Tumor and Normal Tissues

Examples for relatively quantified TUMAPs are shown below.

EXAMPLE 1

Presentation profile for an HLA class I peptide of PTPRZ1 (tyrosinephosphatase, receptor-type, Z polypeptide 1) which is part of aglioblastoma vaccine cocktail. Shown in red are tumor samples relativeto normal tissue samples from different organs. Each bar represents thenormalized median sample area for this peptide while the error bars showthe minimum and maximum replicate area as indicators for the measurementvariation.

EXAMPLE 2 CDC2-001

For the presentation profile for a gastric cancer associated TUMAPencoded by CDK1, reference is made to FIG. 5.

EXAMPLE 3 ASPM-002

For the presentation profile for a gastric cancer associated TUMAPencoded by ASPM reference is made to FIG. 6.

MHC Protein Expression in Different Tissue Entities

The inventors analyzed expression of MHC molecules in different tissueentities to know about the range of MHC expression levels usingbeta-2-microglobulin as representative compound of the ternary MHC alphachain/beta-2-microglobulin/peptide complex (see FIG. 7).

Relative MHC expression (b2m/beta actin) varies only by a factor of 5around the median value b2m/beta actin=0.52 and thus is relativelyconstant between different tissue entities. This range is narrow enoughfor a normalization based on mass spectrometry data.

Normalization of MHC amounts based on mass spectrometry as compared towestern blotting (WB)

TABLE 2 Comparison of WB-based and MS-based normalization of MHC TC001TRCC2216N RCC2216T RCC2223N RCC2223T Relative WB 0.07 2.03 2.58 1.00 0.17normaliz. factor Relative MS 0.05 2.53 1.11 1.00 0.30 normaliz. factorTissue weight [g] 3.56 0.29 0.89 0.65 1.54 IDs for MS 119 540 47 908 877normalization

In summary, MS- and WB-based normalization are very comparable;variation between these normalization methods is only by about a factorof 2. The MS normalization factor improves in accuracy if moreidentifications are used in its calculation. Therefore, MS basednormalization is a reliable method for compensating varying amounts ofMHC molecules obtained from primary tissue immune precipitations.

Quality Assurance

In order to check the quantitative performance continuously, weestablished and introduced seven synthetic non-human peptides forquality control of retention time and signal area.

Since each sample is analyzed using five replicates, peptidequantitation is accompanied by estimation of variation to detectmeasurement problems. Concomitantly, we have established a manualquality control procedure to ensure consistency and correctness of datafor peptides entering the validation.

Example for the determination of the immunogenicity of the peptides andthe expression profiling

Expression Profiling of Genes Encoding the Peptides of the Invention

Not all peptides identified as being presented on the surface of tumorcells by MHC molecules are suitable for immunotherapy, because themajority of these peptides are derived from normal cellular proteinsexpressed by many cell types. Only few of these peptides aretumor-associated and likely able to induce T cells with a highspecificity of recognition for the tumor from which they were derived.In order to identify such peptides and minimize the risk forautoimmunity induced by vaccination the inventors focused on thosepeptides that are derived from proteins that are over-expressed on tumorcells compared to the majority of normal tissues.

The ideal peptide will be derived from a protein that is unique to thetumor and not present in any other tissue. To identify peptides that arederived from genes with an expression profile similar to the ideal onethe identified peptides were assigned to the proteins and genes,respectively, from which they were derived and expression profiles ofthese genes were generated.

RNA Sources and Preparation

Surgically removed tissue specimens were provided by two differentclinical sites (see Example 1) after written informed consent had beenobtained from each patient. Tumor tissue specimens were snap-frozen inliquid nitrogen immediately after surgery and later homogenized withmortar and pestle under liquid nitrogen. Total RNA was prepared fromthese samples using TRI Reagent (Ambion, Darmstadt, Germany) followed bya cleanup with RNeasy (QIAGEN, Hilden, Germany); both methods wereperformed according to the manufacturer's protocol.

Total RNA from healthy human tissues was obtained commercially (Ambion,Huntingdon, UK; Clontech, Heidelberg, Germany; Stratagene, Amsterdam,Netherlands; BioChain, Hayward, Calif., USA). The RNA from severalindividuals (between 2 and 123 individuals) was mixed such that RNA fromeach individual was equally weighted. Leukocytes were isolated fromblood samples of four healthy volunteers.

Quality and quantity of all RNA samples were assessed on an Agilent 2100Bioanalyzer (Agilent, Waldbronn, Germany) using the RNA 6000 PicoLabChip Kit (Agilent).

Microarray Experiments

Gene expression analysis of all tumor and normal tissue RNA samples wasperformed by Affymetrix Human Genome (HG) U133A or HG-U133 Plus 2.0oligonucleotide microarrays (Affymetrix, Santa Clara, Calif., USA). Allsteps were carried out according to the Affymetrix manual. Briefly,double-stranded cDNA was synthesized from 5-8 μg of total RNA, usingSuperScript RTII (Invitrogen) and the oligo-dT-T7 primer (MWG Biotech,Ebersberg, Germany) as described in the manual. In vitro transcriptionwas performed with the BioArray High Yield RNA Transcript Labelling Kit(ENZO Diagnostics, Inc., Farmingdale, N.Y., USA) for the U133A arrays orwith the GeneChip IVT Labelling Kit (Affymetrix) for the U133 Plus 2.0arrays, followed by cRNA fragmentation, hybridization, and staining withstreptavidin-phycoerythrin and biotinylated anti-streptavidin antibody(Molecular Probes, Leiden, Netherlands). Images were scanned with theAgilent 2500A GeneArray Scanner (U133A) or the Affymetrix Gene-ChipScanner 3000 (U133 Plus 2.0), and data were analyzed with the GCOSsoftware (Affymetrix), using default settings for all parameters. Fornormalization, 100 housekeeping genes provided by Affymetrix were used.Relative expression values were calculated from the signal log ratiosgiven by the software and the normal kidney sample was arbitrarily setto 1.0.

The expression profiles of source genes of the present invention thatare highly over-expressed in gastric cancer are shown in FIG. 2.

EXAMPLE 3

In Vitro Immunogenicity for IMA941 MHC Class I Presented Peptides

To get information regarding the immunogenicity of the TUMAPs of thepresent invention, we performed investigations using a well establishedin vitro stimulation platform already described by (Walter, S, Herrgen,L, Schoor, O, Jung, G, Wernet, D, Buhring, H J, Rammensee, H G, andStevanovic, S; 2003, Cutting edge: predetermined avidity of human CD8 Tcells expanded on calibrated MHC/anti-CD28-coated microspheres,J.Immunol., 171, 4974-4978). This way we could show immunogenicity for32 HLA-A*2402 restricted TUMAPs of the invention demonstrating thatthese peptides are T-cell eptiopes against which CD8+ precursor T cellsexist in humans (Table 3).

In Vitro Priming of CD8+T Cells

In order to perform in vitro stimulations by artificial antigenpresenting cells (aAPC) loaded with peptide-MHC complex (pMHC) andanti-CD28 antibody, we first isolated CD8 T cells from fresh HLA-A*24leukapheresis products of healthy donors obtained from the Blood BankTuebingen.

CD8 T cells were either directly enriched from the leukapheresis productor PBMCs (peripheral blood mononuclear cells) were isolated first byusing standard gradient separation medium (PAA, Cölbe, Germany).Isolated CD8 lymphocytes or PBMCs were incubated until use in T-cellmedium (TCM) consisting of RPMI-Glutamax (Invitrogen, Karlsruhe,Germany) supplemented with 10% heat inactivated human AB serum(PAN-Biotech, Aidenbach, Germany), 100 U/ml Penicillin/100 μg/mlStreptomycin (Cambrex, Cologne, Germany), 1 mM sodium pyruvate (CC Pro,Oberdorla, Germany), 20 μg/ml Gentamycin (Cambrex). 2.5 ng/ml IL-7(PromoCell, Heidelberg, Germany) and 10 U/ml IL-2 (Novartis Pharma,Nürnberg, Germany) were also added to the TCM at this step. Isolation ofCD8+ lymphocytes was performed by positive selection using CD8MicroBeads (Miltenyi Biotec, Bergisch-Gladbach, Germany).

Generation of pMHC/anti-CD28 coated beads, T-cell stimulations andreadout was performed as described before(Walter et al. 4974-78) withminor modifications. Briefly, biotinylated peptide-loaded recombinantHLA-A*2402 molecules lacking the transmembrane domain and biotinylatedat the carboxy terminus of the heavy chain were produced. The purifiedcostimulatory mouse IgG2a anti human CD28 Ab 9.3 (Jung, Ledbetter, andMuller-Eberhard 4611-15) was chemically biotinylated usingSulfo-N-hydroxysuccinimidobiotin as recommended by the manufacturer(Perbio, Bonn, Germany). Beads used were 5.6 μm large streptavidincoated polystyrene particles (Bangs Laboratories, Ill., USA). pMHC usedas controls were A*0201/MLA-001 (peptide ELAGIGILTV (SEQ ID No. 26) frommodified Melan-A/MART-1) and A*0201/DDX5-001 (YLLPAIVHI (SEQ ID No. 27)from DDX5), respectively.

800.000 beads/200 μl were coated in 96-well plates in the presence of600 ng biotin anti-CD28 plus 200 ng relevant biotin-pMHC (high densitybeads). Stimulations were initiated in 96-well plates by co-incubating1×10⁶ CD8+ T cells with 2×10⁵ washed coated beads in 200 μl TCMsupplemented with 5 ng/ml IL-12 (PromoCell) for 3-4 days at 37° C. Halfof the medium was then exchanged by fresh TCM supplemented with 80 U/mlIL-2 and incubating was continued for 3-4 days at 37° C. Thisstimulation cycle was performed for a total of three times. Finally,multimeric analyses were performed by staining the cells withLive/dead-Aqua dye (Invitrogen, Karlsruhe, Germany), CD8-FITC antibodyclone SK1 (BD, Heidelberg, Germany) and PE- or APC-coupled A*2402 MHCmultimers. For analysis, a BD LSRII SORP cytometer equipped withappropriate lasers and filters was used. Peptide specific cells werecalculated as percentage of total CD8+ cells. Evaluation of multimericanalysis was done using the FlowJo software (Tree Star, Oreg., USA). Invitro priming of specific multimer+ CD8+ lymphocytes was detected byappropriate gating and by comparing to negative control stimulationsImmunogenicity for a given antigen was detected if at least oneevaluable in vitro stimulated well of one healthy donor was found tocontain a specific CD8+ T-cell line after in vitro stimulation (i.e.this well contained at least 1% of specific multimer+ among CD8+ T-cellsand the percentage of specific multimer+ cells was at least 10× themedian of the negative control stimulations).

In vitro Immunogenicity for the Peptides

For tested HLA class I peptides, in vitro immunogenicity could bedemonstrated for 25 peptides by generation of peptide specific T-celllines. Exemplary flow cytometry results after TUMAP-specific multimerstaining for two peptides of the invention are shown in FIG. 3 togetherwith a corresponding negative control. Results for the 25 peptides fromthe invention are summarized in Table 3.

TABLE 3 In vitro immunogenicity of HLA class I peptides of the inventionResults of in vitro immunogenicityexperiments conducted by the inventorsshow the percentage of positive testeddonors and wells among evaluable. At least two donors and 24 wells wereevaluable for each peptide. Positive Positive donors/ wells/ SEQ donorswells ID tested tested NO: Antigen Sequence [%] [%]  1 CDC2-001LYQILQGIVF  88 28  2 ASPM-002 SYNPLWLRI  63 31  3 MMP3-001 VFIFKGNQF  13 1  4 MET-006 SYIDVLPEF  63 22  5 UCHL5-001 NYLPFIMEL  75 14  6MST1R-001 NYLLYVSNF  50 14  7 KIF2C-001 IYNGKLFDLL  13  2  8 SMC4-001HYKPTPLYF  75  9  9 PROM1-001 VWSDVTPLTF  83 26 10 MMP11-001 NYLLYVSNF 33 11 11 NFYB-001 VYTTSYQQI  50  7 12 ASPM-001 RYLWATVTI  17  3 13PLK4-001 QYASRFVQL  60  5 14 ABL1-001 TYGNLLDYL  50 13 15 ATAD2-001AYAIIKEEL  50  4 16 AVL9-001 FYISPVNKL 100 50 17 HSP90B1-001 KYNDTFWKEF 50  4 18 MUC6-001 NYEETFPHI  50 21 19 NUF2-001 VYGIRLEHF 100 25 20NUF2-002 RFLSGIINF  50  4 21 PPAP2C-001 AYLVYTDRL 100 54 22 SIAH2-001VFDTAIAHLF  50  4 23 UQCRB-001 YYNAAGFNKL 100 38 24 IQGAP3-001 VYKVVGNLL100 24 25 ERBB3-001 VYIEKNDKL  83 15

1. A method for identification and label-free quantification of MHCligand peptides on a primary tissue sample, comprising: a) providing atleast one diseased primary tissue sample and at least one sample ofprimary healthy tissue optionally corresponding to diseased tissue, b)isolating one or more MHC ligand peptides from said sample, c)performing an HPLC-MS analysis on said MHC ligand peptides, d)extracting a precursor ion signal intensity area for each signal, asderived from c), e) identifying one or more sequences of said MHC ligandpeptides by fragment spectra clustering and database search, in order togroup areas between different runs and samples, and normalizing areaswithin replicate runs to compensate technical performance and/orsensitivity differences between replicate runs resulting in averagequantities for each peptide per sample including error estimates, f)Assigning said sequences to MHC alleles in order to generateallele-specific sequence subgroups for a comparison between differentsamples, and normalizing between different samples using allele-specificsubgroups to account for different sample sizes or MHC expression levelswherein a result is relative quantities comparable between samples, g)performing a data quality control based on peptide reproducibility forevery sample, verifying that a total number of identified sequences anda number of sequences with small variances in areas is as high aspossible, h) calculating presentation profiles and scores in order todetermine a potential overpresentation ofMHC ligand peptides, i)controlling a quality of identifications and area estimates, optionallyby visual inspection of peptide identification results and of areareproducibilites, or statistical analysis, j) comparing value detectedin said at least one diseased primary tissue sample with the valueobtained from said at least one primary healthy tissue, and k)quantifying said MHC ligand peptide.
 2. The method according to claim 1,wherein said MHC ligand peptide comprises a tumor associated peptide(TAA) or disease associated peptide (DAA).
 3. The method according toclaim 1, further comprising a selection of overrepresented and/ortumor-specific MHC ligand peptides.
 4. The method according to claim 1,wherein said diseased sample is a tumor sample or a sample of infectedtissue.
 5. The method according to claim 1, wherein controlling qualityis performed using an automate and/or by manual inspection.
 6. Themethod according to claim 1, further comprising at least one selectedfrom the group consisting of: in d) aligning the retention time toassign corresponding signals within replicate runs without a knowledgeabout sequences, and/or assigning corresponding signals betweendifferent samples by retention time alignment without knowledge aboutsequences, in h) implementing relative presentation data from more thanone peptide per gene to calculate a gene-centered over-presentationscore, and in i) performing a quality control based on a spiked peptide.7. The method according to claim 1, wherein said method is performed invitro.
 8. The method according to claim 1, wherein of said method isperformed in the order as indicated.
 9. The method according to claim 1,wherein said method consists of the steps as indicated.
 10. The methodaccording to claim 1, further comprising synthesizing said at least oneMHC ligand peptide as identified and/or quantified by said method on asynthesizer or manually.
 11. The method according to claim 10, furthercomprising testing said MHC ligand peptide as synthesized peptide forimmunogenicity.
 12. The method according to claim 1, wherein said sampleis derived from one individual.
 13. The method according to claim 12,further comprising generating a personalized MHC ligand profile,optionally a personalized disease-specific MHC ligand profile, based onsaid MHC ligand peptides as identified and/or quantified.
 14. The methodaccording to claim 1, wherein said total number of identified sequencesand number of sequences with small variances in areas is higher than 5,optionally higher than 25.